Data Science
Insight from data that is too big to read
We help companies efficiently understand gigantic data from multiple sources and derive valuable insights to make smarter, data-driven decisions, with models designed for production, not just for a notebook.
- Data sources unified
- Structured and unstructured
- Delivery model
- Pilot first, then scale
What this practice actually does for you.
We help companies efficiently understand gigantic data from multiple sources and derive valuable insights that lead to smarter decisions: forecasting, computer vision, document understanding, and anomaly detection.
Every engagement starts as a scoped pilot on your own data. You see accuracy and business impact on real cases before committing to a full rollout.
Production is where most AI projects fail, so monitoring, retraining, and human review are part of the delivery rather than an afterthought.
What we deliver
- Forecasting and demand modelling
- Computer vision and OCR pipelines
- Natural language processing
- Anomaly and fraud detection
- MLOps and model monitoring
Toolkit
- Python
- PyTorch
- OpenCV
- Tesseract & OCR engines
- MLflow
- Google Cloud
The data science delivery path.
Four stages, each with a defined output you review before the next one starts.
- 01
Feasibility
We review available data, define the success metric, and confirm whether a model is genuinely the right answer.
- 02
Pilot
A working model on a representative sample, measured against a baseline so the business value is explicit.
- 03
Productionise
Pipelines, APIs, and human-in-the-loop review built into your systems, with data contracts and access controls.
- 04
Monitor & retrain
Drift monitoring, scheduled retraining, and accuracy reporting so performance does not quietly decay.
Where clients start with this service.
Demand and inventory forecasting
Predict demand per SKU and location to reduce both stock-outs and slow-moving inventory.
Document understanding
OCR and NLP that read invoices, contracts, and statements and return structured, validated records.
Computer vision in operations
Customer movement, safety compliance, and quality inspection measured from cameras already installed on site.
Data Science: questions we get asked.
Less than most teams assume. For vision and document work a few hundred representative examples is often enough for a meaningful pilot; forecasting needs a couple of years of history.
Ready to scope your data science project?
Tell us what the process looks like today and we will come back with an approach, a timeline, and an honest view of the effort involved.